In acknowledgement of this point, RL research has long sought workable methods for non-linear function approximation. Although a variety of approaches have been explored over the years – often treating the representation learning problem independent of the underlying RL problem [1]}, [2]} – a longstanding aspiration has been to perform adaptive nonlinear function approximation using deep neural networks.
